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Contextual bandit optimization of super-resolution microscopy

Published onMay 27, 2022
Contextual bandit optimization of super-resolution microscopy
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Abstract

The online optimization of optical microscopy parameters aims at learning the set of imaging parameters while the experiment unfolds. The success of this task relies on the trade-off between multiple confounding objectives and may vary depending on the experimental setting. In this work, we frame the optimization problem as a multi-armed bandit framework with contextual information about the sample to identify optimal sample-dependant imaging parameters. This allows to take into consideration the current state of the sample and choose the imaging parameters accordingly.


Article ID: 2022S01

Month: May

Year: 2022

Address: Online

Venue: Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association

URL:https://caiac.pubpub.org/pub/6th7w3yo

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